論文

• S. Manzhos, Q.-G. Chen, W.-Y. Lee, Yoon Hee Joo, Manabu Ihara, C.-C. Chueh. Computational investigation of the potential and limitations of machine learning with neural network circuits based on synaptic transistors, The Journal of Physical Chemistry Letters, Volume 15, Issue 27, June 2024.
• Kakaraparthi Kranthiraja, Waner He, Hao-Wei Yu, Zhen Feng, Naoya Nozaki, Hidetoshi Matsumoto, Ming-Hsuan Yu, Yong Li, Sergei Manzhos, Mats R. Andersson, Chu-Chen Chueh, Tsuyoshi Michinobu, Prashant Sonar. Diketopyrrolopyrrole-Dioxo-Benzodithiophene-Based Multi-Functional Conjugated Polymers for Organic Field Effect Transistors and Perovskite Solar Cells, Sol. RRL, Wiley, Vol. 8, No. 14, 2400185, May 2024.  公式リンク  
  
• Takuma Okamoto, A. Sorkin, Keisuke Kameda, Manabu Ihara, H. Wang, Sergei Manzhos. Natural-like generation of grain boundary models and the combined effects of microstructural elements and lithiation on the plastic behavior of TiO2: a computational study, Computational Materials Science, Volume 239, Apr. 2024.
• Hiroo Suzuki, Jun Kametaka, Shinya Nakahori, Yuichiro Tanaka, Mizuki Iwahara, Haolu Lin, Sergei Manzhos, Aung Ko Ko Kyaw, Takeshi Nishikawa, Yasuhiko Hayashi. N-DMBI Doping of Carbon Nanotube Yarns for Achieving High n-Type Thermoelectric Power Factor and Figure of Merit, Small methods, 2301387, Mar. 2024.
• Methawee Nukunudompanich, H. Yoon, Lee Hyojae, Keisuke Kameda, Manabu Ihara, Sergei Manzhos. Machine learning of properties of lead-free perovskites with a neural network with additive kernel regression-based neuron activation functions, MRS Advances, Jan. 2024.
• S. Manzhos, M. Ihara. Degeneration of kernel regression with Matern kernels into low-order polynomial regression in high dimension, The Journal of Chemical Physics, Volume 160, Issue 2, Jan. 2024.
• D. Koch, M. Pavanello, X. Shao, M. Ihara, P. W. Ayers, C. F. Matta, S. Jenkins, S. Manzhos. The analysis of electron densities: from basics to emergent applications, Chem. Rev., 2024.
• J. Luder, M. Ihara, S. Manzhos. A machine-learned kinetic energy model for light weight metals and compounds of group III-V elements, Electronic Structure, 2024.
• S. Manzhos, M. Ihara. Orders-of-coupling representation achieved with a single neural network with optimal neuron activation functions and without nonlinear parameter optimization, Artificial Intelligence Chemistry, Volume 1, Issue 2, Dec. 2023.
• S. Manzhos, J. Lueder, M. Ihara. Machine learning of kinetic energy densities with target and feature smoothing: better results with fewer training data, The Journal of Chemical Physics, Volume 159, Issue 23, Dec. 2023.
• S. Manzhos, M. Ihara. A controlled study of the effect of deviations from symmetry of the potential energy surface (PES) on the accuracy of the vibrational spectrum computed with collocation, The Journal of chemical Physics, Volume 159, Issue 21, Dec. 2023.
• Martin-Isbjörn Trappe, William C. Witt, Sergei Manzhos. Atoms, dimers, and nanoparticles from orbital-free density-potential-functional theory, Physical Review A (atomic, molecular, and optical physics and quantum information), Vol. 108, p. 062802, Dec. 2023.
• S. Manzhos, T. Carrington, M. Ihara. Orders of coupling representations as a versatile framework for machine learning from sparse data in high-dimensional spaces, Artificial Intelligence Chemistry, Volume 1, Issue 2, Dec. 2023.
• Ruicheng Li, G. Budiutama, S. Manzhos, M. Ihara. Hybrid DFTB – Molecular Mechanics approach: applicability to optical properties, 34th IUPAP Conference on Computational Physics (CCP2023), Proceeding of 34th IUPAP Conference on Computational Physics (CCP2023), Springer Proceedings in Physics, Nov. 2023.
• S. Manzhos, M. Ihara. Neural networks with rules-based parameters and without nonlinear optimization: comparison of fixed-shaped and optimized neuron activation functions, 34th IUPAP Conference on Computational Physics (CCP2023), Proceeding of 34th IUPAP Conference on Computational Physics (CCP2023), Springer Proceedings in Physics, Nov. 2023.
• Gurpreet Singh Selopal, Omar Abdelkarim, Jasneet Kaur, Jiabin Liu, Lei Jin, Zhangsen Chen, Fabiola Navarro-Pardo, Sergei Manzhos, Shuhui Sun, Aycan Yurtsever, Hadis Zarrin, Zhiming M. Wang, Federico Rosei. Surface engineering of two-dimensional hexagonal boron-nitride for optoelectronic devices, Nanoscale, Vol. 15, pp. 15810-15830, Sept. 2023.
• S. Manzhos, M. Ihara. Rectangularization of Gaussian process regression for optimization of hyperparameters, Machine Learning with Applications, Volume 13, Sept. 2023.
• S. Manzhos, M. Ihara. Neural network with optimal neuron activation functions based on additive Gaussian process regression, The Journal of Physical Chemistry A, Volume 127, Issue 37, Page 7823–7835, Sept. 2023.
• M. Nukunudompanich, K. Suzuki, S. Manzhos, K. Kameda, M. Ihara. Nano-scale smooth surface of a compact-TiO2 layer via spray pyrolysis for controlling perovskite grain sizes in perovskite solar cell, RSC Advances, Issue 40, page 27686 - 27695, Sept. 2023.
• G. Budiutama, Ruicheng, M. Ihara*, S. Manzhos*. Hybrid Density Functional Tight Binding (DFTB) – molecular mechanics approach for a low-cost expansion of DFTB applicability, Journal of Chemical Theory and Computation, Volume 19, Issue 15, Page 5189–5198, July 2023.
• Waner He, Qian Liu, Sultan Otep, Hidetoshi Matsumoto, Sergei Manzhos, Prashant Sonar, Ang Ko Ko Kyaw, Tsuyoshi Michinobu. Tetramethylammonium Iodide Additive for Enhancing the Charge Carrier Mobilities of Diketopyrrolopyrrole-Based Conjugated Polymer in Ambipolar Organic Field-Effect Transistors, Chin. J. Chem., Wiley-VCH, Vol. 41, Issue 9, pp. 1028-1036, May 2023.  公式リンク  
  
• P. Sundarapura, S. Manzhos, M. Ihara. Clarifying the effects of nanoscale porosity of silicon on the bandgap and alignment: a combined molecular dynamics – density functional tight binding computational study, Physical Chemistry Chemical Physics, Issue 20, May 2023.
• T. Okubo, T. Shimizu, K. Hasegawa, Y. Kikuchi, S. Manzhos, M. Ihara. Factors affecting the techno-economic and environmental performance of on-grid distributed hydrogen energy storage systems with solar panels, Energy, Volume 269, Apr. 2023.
• A. Sorkin, Y. Guo, S. Manzhos, M. Ihara, H. Wang. Non-invasive improvement of machining by reversible electrochemical doping: a proof of principle with computational modeling on the example of lithiation of TiO2, Materials Chemistry and Physics, Volume 295, Feb. 2023.
• S. Manzhos, M. Ihara. The loss of the property of locality of the kernel in high-dimensional Gaussian process regression on the example of the fitting of molecular potential energy surfaces, The Journal of Chemical Physics, Volume 158, Issue 4, 044111, Jan. 2023.
• S. Manzhos, S. Tsuda, M. Ihara. Machine learning in computational chemistry: interplay between (non)linearity, basis sets, and dimensionality, Physical Chemistry Chemical Physics, 25, Issue 3, Page 1546-1555, 2023.
• S. Manzhos, M. Ihara. Optimization of hyperparameters of Gaussian process regression with the help of low-order high-dimensional model representation: application to a potential energy surface, Journal of Mathematical Chemistry, 61, 7-20, 2023.
• Thu-Trang Do, Yasunori Takeda, Tomohito Sekine, Yogesh Yadav, Sergei Manzhos, Krishna Feron, Samarendra P Singh, Shizuo Tokito, Prashant Sonar. Bottom gate top contact organic transistors using thiophene and furan flanked diketopyrrolopyrrole polymers and its comparative study, Flexible and Printed Electronics, Volume 7, Number 3, Aug. 2022.
• Thu-Trang Do, Yasunori Takeda, Tomohito Sekine, Yogesh Yadav, Sergei Manzhos, Krishna Feron, Samarendra P Singh, Shizuo Tokito, Prashant Sonar. Bottom gate top contact organic transistors using thiophene and furan flanked diketopyrrolopyrrole polymers and its comparative study, Flexible and Printed Electronics, Volume 7, Number 3, Aug. 2022.
• H. Kulik, T. Hammerschmidt, J. Schmidt, S. Botti, M. A. L. Marques, M. Boley, M. Scheffler, M. Todorović, P. Rinke, C. Oses, A. Smolyanyuk, S. Curtarolo, A. Tkatchenko, A. Bartok, S. Manzhos, M. Ihara, T. Carrington, J. Behler, O. Isayev, M. Veit, A. Grisafi, J. Nigam, M. Ceriotti, K. T. Schütt, J. Westermayr, M. Gastegger, R. Maurer, B. Kalita, K. Burke, R. Nagai, R. Akashi, O. Sugino, J. Hermann, F. Noé, S. Pilati, C. Draxl, M. Kuban, S. Rigamonti, M. Scheidgen, M. Esters, D. Hicks, C. Toher, P. Balachandran, I. Tamblyn, S. Whitelam, C. Bellinger, L. M. Ghiringhelli. Roadmap on machine learning in electronic structure, Electronic Structure, Volume 4, Number 2, Aug. 2022.
• D. Koch, S. Manzhos*, M. Chaker. The role of local DFT+U minima in the first-principles modeling of the metal-insulator transition in vanadium dioxide, The Journal of Physical Chemistry A, Volume 126, Issue 22, 3604-3611, June 2022.
• Y. J. Lee, Y. Zhang, A. Sorkin, S. Manzhos, H. Wang. Effect of a weak magnetic field on ductile-brittle transition in micro-cutting of single-crystal calcium fluoride, Journal of Materials Science & Technology, Volume 112, Page 96-113, June 2022.
• S. Manzhos*, M. Ihara. Computational vibrational spectroscopy of molecule-surface interactions: what is still difficult and what can be done about it, Physical Chemistry Chemical Physics, 24, 15158-15172, May 2022.
• Gekko Patria Budiutama, Sergei Manzhos, Manabu Ihara. Effective Passivation of TiO2/Si by Interlayer SiOx Controlled by Scanning Zone Annealing for Perovskite/Si Tandem Solar Cell, Solar Energy, Volume 236, Page 772-781, Apr. 2022.
• Sergei Manzhos, Manabu Ihar. Advanced Machine Learning Methods for Learning from Sparse Data in High-Dimensional Spaces: A Perspective on Uses in the Upstream of Development of Novel Energy Technologies, Physchem, 2, 2, 72-95, Mar. 2022.
• Qian Liu, Waner He, Yongqiang Shi, Sultan Otep, Wen Liang Tan, Sergei Manzhos, Christopher R. McNeill, Xugang Guo, Prashant Sonar*, Tsuyoshi Michinobu*, Aung Ko Ko Kyaw*. Directional carrier polarity tunability in ambipolar organic transistors based on diketopyrrolopyrrole and bithiophene imide dual-acceptor semiconducting polymers, Chemistry of Materials, American Chemical Society, Vol. 34(7), No. 7, pp. 3140-3151, Mar. 2022.
• O. Ren, M. Ali Boussaidi, D. Voytsekhovsky, M. Ihara, S. Manzhos. Random Sampling High Dimensional Model Representation Gaussian Process Regression (RS-HDMR-GPR) for representing multidimensional functions with machine-learned lower-dimensional terms allowing insight with a general method, Computer Physics Communications, Vol. 271, Feb. 2022.
• Sergei Manzhos, Eita Sasaki, Manabu Ihara. Easy representation of multivariate functions with low-dimensional terms via Gaussian process regression kernel design: applications to machine learning of potential energy surfaces and kinetic energy densities from sparse data, Machine Learning: Science and Technology, Jan. 2022.
• L. Hao*, S. Manzhos*, Z. Zhang. Theoretical insight into diamond doping and its possible effect on diamond tool wear during cutting of steel, Frontiers in Materials, Volume 8, 806466, Dec. 2021.
• Keisuke Kameda, Sergei Manzhos, Manabu Ihara. Carbon/air secondary battery system and demonstration of its charge-discharge, Journal of Power Sources, Vol. 516, Dec. 2021.
• Daniel Koch, Mohamed Chaker, Manabu Ihara, Sergei Manzhos. Density-Based Descriptors of Redox Reactions Involving Transition Metal Compounds as a Reality-Anchored Framework: A Perspective, Molecules, volume 26, issue 18, Sept. 2021.
• Sergei Manzhos, Tucker Carrington Jr. Neural Network Potential Energy Surfaces for Small Molecules and Reactions, Chemical Reviews, Vol. 121, Issue 16, 10187–10217, Aug. 2021.
• S. Manzhos, G. Giorgi, J. Lüder, M. Ihara. Modeling of plasmonic properties of nanostructures for next generation solar cells and beyond, Advances in Physics: X, volume 6, issue 1, Aug. 2021.
• Q. Liu, D. Moghe, G. Sardar, S. Manzhos, S. E. Bottle, A. K. K. Kyaw, D. Kabra, P. Sonar. Structural geometry variation of 1,4-naphthalene-based copolymers to tune the device performance of PVK-host-based OLEDs, Polymers, 13(17), Aug. 2021.
• Pandeng Li, Jin Fang, Yusheng Wang, Sergei Manzhos, Lei Cai, Zheheng Song, Yajuan Li, Tao Song, Xuechun Wang, Xia Guo, Maojie Zhang, Dongling Ma, Baoquan Sun. Synergistic Effect of Dielectric Property and Energy Transfer on Charge Separation in Non-Fullerene-Based Solar Cells, Angewandte Chemie International Edition, Volume 60, Issue 27, p. 15054-15062, June 2021.
• Hao-Sheng Lin, Yue Ma, Rong Xiang, Sergei Manzhos, Il Jeon, Shigeo Maruyama, Yutaka Matsuo. One-step direct oxidation of alkoxy to ketone for evaporable fullerene-fused ketone as efficient electron-transport materials, Communications Chemistry, 4, 74, May 2021.
• Sergei Manzhos, Chu-Chen Chueh, Giacomo Giorgi, Takaya Kubo, Saianand Gopalan, Johann Lüder, Prashant Sonar, Manabu Ihara. Materials Design and Optimization for Next Generation Solar Cell and Light-Emitting Technologies, The Journal of Physical Chemistry Letters, Volume 12, Issue 19, 4638–4657, May 2021.
• D. Koch, S. Manzhos. Can doping of transition metal oxide cathode materials increase achievable voltages with multivalent metals?, International Journal of Quantum Chemistry, Volume 121, Issue 2, e26439, Jan. 2021.
• Qian Liu, Sudam Chavhan, Hantang Zhang, Huabin Sun, Aidan J. Brock, Sergei Manzhos, Yingqian Chen, Krishna Feron, Steven E. Bottle, John C. McMurtrie, Jwo-Huei Jou, Ho-Shin Chen, Mangey Ram Nagar, Wenping Hu, Yong-Young Noh, Yonggang Zhen, Prashant Sonar. Short Alkyl Chain Engineering Modulation on Naphthalene Flanked Diketopyrrolopyrrole toward High-Performance Single Crystal Transistors and Organic Thin Film Displays, Advanced Electronic Materials, Volume 7, Issue 1, 2000804, Jan. 2021.

著書

• T. Carrington, S. Manzhos, M. Ihara, T. Carrington. “Machine learning for vibrational spectroscopy“, Quantum Chemistry in the Age of Machine Learning (Ed. P. Dral), Elsevier, 2022.
• S. Manzhos, G. Giorgi, J. Lüder, M. Ihara. Modeling methods for plasmonic effects in halide perovskite-based systems for photonics applications, Halide Perovskites for Photonics, AIP Publishing, 11-1–1-52, 2021.

国際会議発表 (査読有り)

• Sergei Manzhos, Manabu Ihara. Neural networks without nonlinear optimization and with optimized neuron activation functions built with Gaussian processes, The 33rd Annual Meeting of the Japanese Neural Network Society (JNNS2023), Sept. 2023.
• Gekko Budiutama, Sergei Manzhos, Manabu Ihara. Hybrid Density Functional based Tight Binding-Molecular Mechanics Approach with Machine Learned Potentials for Large-Scale Materials Modeling, 2022 MRS Fall Meeting & Exhibit, Dec. 2022.
• Gekko Budiutama, Sergei Manzhos, Manabu Ihara. Investigation of expressive power of a neural network architecture suited for optical neural networks, SPIE/COS Photonics Asia 2022, PROCEEDINGS VOLUME 12318, SPIE/COS PHOTONICS ASIA 2022, Holography, Diffractive Optics, and Applications XII, Dec. 2022.
• A. Sorkin, Y. Guo, M. Ihara, S. Manzhos, H. Wang. Reversible electrochemical lithiation as a way to facilitate the machining of ceramics: a computational study, the Asian Conference on Electrochemical Power Sources 11 (ACEPS’11), Dec. 2022.
• Gekko Budiutama, Sergei Manzhos, Manabu Ihara. Investigation of expressive power of a neural network architecture suited for optical neural networks, SPIE/COS Photonics Asia 2022, Dec. 2022.
• Sergei Manzhos, Manabu Ihara. Non-parametric machine learning for orbital-free DFT simulations, 46th International Conference and Expo on Advanced Ceramics and Composites (ICACC2022), Jan. 2022.
• Keisuke Kameda, Takaaki Ariga, Sergei Manzhos, Manabu Ihara. Enhancing the bond valence representation for prescreening of solid state ionic conductors, Electronic Materials and Applications 2022 (EMA 2022), Jan. 2022.
• D. Koch, S. Manzhos, M. Chaker. Modeling the Metal-Insulator Transition in Vanadium Dioxide from First Principles: How Local Minima in DFT+U Can Affect Conclusions, 46th International Conference and Expo on Advanced Ceramics and Composites (ICACC2022), Jan. 2022.
• S. Hill, T. Carrington, S. Manzhos, M. Ihara. Local Gaussian process regression for interatomic potentials, 2021 MRS Fall Meeting & Exhibit, Dec. 2021.
• G. Budiutama, S. Manzhos, M. Ihara. Role of amorphous silica interlayer in enhancing the performance of perovskite/silicon tandem solar cells, 2021 MRS Fall Meeting & Exhibit, Dec. 2021.
• E. Sasaki, M. Ihara, S. Manzhos. Structured Gaussian Process Regression models to address difficulties in modeling very high dimensional data with product kernels, 2021 MRS Fall Meeting & Exhibit, Dec. 2021.
• S. Manzhos, M. Ihara. Machine learning for large-scale ab initio simulations with orbital-free DFT, 31st Annual Meeting of MRS-J, Dec. 2021.

国内会議発表 (査読有り)

• S Manzhos, S Tsuda, H Lee, M Ihara. Hybrid models combining neural networks (NN), Gaussian process regressions (GPR), and high-dimensional model representations (HDMR) for more powerful machine learning., 人工知能学会全国大会, July 2023.

国際会議発表 (査読なし・不明)

• Sergei Manzhos. Advancing Orbital-Free DFT and DFTB for large-scale ab initio materials modeling with machine learning, Seminar at the Department of Physics, Sept. 2024.
• J. Luder, M. Ihara, sergei manzhos. Kinetic energy density-based machine learning models of kinetic energy using gradient expansion based features, Towards Routine Orbital-free Large-Scale Quantum-Mechanical Modelling of Materials, Sept. 2024.
• Sergei Manzhos. Materials Informatics with Hybrid Machine Learning Methods, The Zhejiang University-University of Illinois Urbana-Champaign Institute, Sept. 2024.
• Sergei Manzhos. At the Intersection of Material Informatics and Machine Learning Method Development, Sept. 2024.
• Takuma Okamoto, Anastassia Sorkin, Keisuke Kameda, Manabu Ihara, Hao Wang, Sergei Manzhos. Computational models of grain structures of titania with nature-like grain distributions, 14th International Conference on Ceramic Materials and Components for Energy and Environmental Systems, Aug. 2024.
• Kexin Chen, William Dawson, Takahito Nakajima, Aulia Sukma Hutama, Keisuke Kameda, Sergei Manzhos, Manabu Ihara. Bandstructure modulation and molecular adsorption properties of zirconia nanoparticles: a large-scale electronic structure study, 14th International Conference on Ceramic Materials and Components for Energy and Environmental Systems, Aug. 2024.
• Sergei Manzhos, Manabu Ihara. Additive kernel based methods for stable machine learning from sparse data: from materials informatics to orbital-free DFT, 14th International Conference on Ceramic Materials and Components for Energy and Environmental Systems, Aug. 2024.
• Sergei Manzhos. Machine learning with kernel methods in high dimensional spaces, Seminar at the Department of Chemistry, June 2024.
• Sergei Manzhos, Manabu Ihara. Hybrid approaches to machine learning from small datasets for applications from materials informatics to large-scale DFT, The 2nd Annual CEMDI Symposium, May 2024.
• Sergei Manzhos, Manabu Ihara. Reliable machine learning from sparse data in high dimension with additive kernel based methods, Chemical Compound Space Conference 2024 (CCSC 2024), May 2024.
• Ruicheng Li, Gekko Budiutama, Keisuke Kameda, Sergei Manzhos, Manabu Ihara. Exploring The Application of Hybrid DFTB-Molecular Mechanics Approach to Computing Optical Properties, 2024 MRS Spring Meeting & Exhibit, May 2024.
• Takuma Okamoto, Anastassia Sorkin, Keisuke Kameda, Wang Hao, Manabu Ihara, Sergei Manzhos. Modelling of Natural-Like Grain Generation of TiO2 and its effect on Band Structures, 2024 MRS Spring Meeting & Exhibit, May 2024.
• Kexin Chen, William Dawson, Takahito Nakajima, Aulia Hutama, Keisuke Kameda, Sergei Manzhos, Manabu Ihara. Effects of Nanosizing of Zirconia and Bandstructure Modulation on Catalytic Activity: Insights from a Combined Density Functional Tight Binding – Order(N) Density Functional Theory Study, 2024 MRS Spring Meeting & Exhibit, May 2024.
• Ruicheng Li, Keisuke Maeda, Man-Fai Ng, Keisuke Kameda, Sergei Manzhos, Manabu Ihara. Exploring Carbon Nanoflake Based Materials for Charge Transport Layers of Perovskite Solar Cells: A Combined DFT-DFTB Study Including Effects of Solid-State Packing, 2024 MRS Spring Meeting & Exhibit, May 2024.
• Nukunudompanich, H. Yoon, L. Hyojae, K. Kameda, M. Ihara, S. Manzhos. Machine learning of properties of perovskites with an NN with additive kernel GPR-based neuron activation functions, Chemical Compound Space Conference 2024 (CCSC 2024), May 2024.
• Sergei Manzhos. Insightful machine learning beyond traditional neural networks and kernel regression, Mar. 2024.
• Sergei Manzhos. Beyond silicon solar cells, Mar. 2024.
• Sergei Manzhos. Machine learning for advancing large scale ab initio materials modeling, Feb. 2024.
• MANZHOS SERGEI. Machine learning beyond off-the-shelf methods, Feb. 2024.
• Sergei Manzhos. Beyond neural networks and kernel regression: hybrid methods for machine learning from sparse data in high-dimensional spaces, Feb. 2024.
• S. Manzhos, M, Ihara. Machine learning in computational chemistry beyond off-the-shelf methods: how to cut the cost, handle overfitting, and obtain elements of insights, International Workshop on Massively Parallel Programming for Quantum Chemistry and Physics (MPQCP 2024), Jan. 2024.
• Sergei Manzhos, Manabu Ihara. Neural networks with optimized neuron activation functions and without nonlinear optimization or how to prevent overfitting, cut CPU cost and get physical insight all at once, 2023 MRS Fall Meeting & Exhibit, Nov. 2023.
• Sergei Manzhos, Takuma Okamoto, Anastasia Sorkin, Keisuke Kameda, Manabu Ihara, Hao Wang. Effect of naturally generated microstructure of a ceramic on ion transport: lithiation of titania, 2023 MRS Fall Meeting & Exhibit, Nov. 2023.
• Ruicheng Li, Keisuke Kameda, Sergei Manzhos, Manabu Ihara. Carbon Nanoflake Based Materials for Charge Transport Layers of Perovskite Solar Cells: Insight from Atomistic Modeling into Nanosizing and Functionalization Suitable for Electron and Hole Transport, 2023 MRS Fall Meeting & Exhibit, Nov. 2023.
• Sergei Manzhos. Metal ion batteries and elements of solid state ionics, Sept. 2023.
• Sergei Manzhos. Materials informatics for materials for new energy, Sept. 2023.
• Sergei Manzhos, Manabu Ihara. Building robust orders-of-coupling representations with machine learning, The 34th IUPAP Conference on Computational Physics (CCP2023), Aug. 2023.
• Gekko Budiutama, Ruicheng Li, Sergei Manzhos, Manabu Ihara. Combining Density Functional Tight Binding (DFTB) with empiric potentials for large-scale semiempirical materials modeling, The 34th IUPAP Conference on Computational Physics (CCP2023), Aug. 2023.
• S. Manzhos. Neural networks with additive Gaussian processes based neuron activation functions: combining high expressive power of a NN with robustness of a linear regression, Seminar at the Department of Mechanical Engineering, National University of Singapore, July 2023.
• Anastassia Sorkin, Jiei Yasumoto, Takuma Okamoto, Sergei Manzhos, Hao Wang, Manabu Ihara. Generation of microstructure of perovskite solar cell materials from molecular dynamics, ICMAT 2023, June 2023.
• Takaaki Ariga, Sergei Manzhos, Manabu Ihara. Enhancement of the bond valence method for rapid screening of solid state ionic conductors with machine learning, ICMAT 2023, June 2023.
• S. Manzhos. Machine learning in computational chemistry: the connections, Seminar at the Department of Chemistry, University of Perugia, May 2023.
• Sergei Manzhos. Computation-assisted and -guided development of renewable energy technologies, King Mongkut’s Institute of Technology Ladkrabang, Mar. 2023.
• Sergei Manzhos. Beyond silicon solar cells, King Mongkut’s Institute of Technology Ladkrabang,, Mar. 2023.
• Panus Sundarapura, Manabu Ihara, Sergei Manzhos. Clarifying the effects of nanostructured porosity of silicon on the band gap and band alignment: a computational study, the 70th JSAP Spring Meeting,, Mar. 2023.
• Sergei Manzhos, Manabu Ihara. Machine learning of potentials and functionals from sparse data and how to get insight with a black box method, 29th Canadian Symposium on Theoretical and Computational Chemistry (CSTCC), June 2022.
• Sergei Manzhos. Machine learning in computational chemistry: the connections, June 2022.
• T. Ariga, K. Kameda, M. Ihara, S. Manzhos. Machine learning based enhancement of the bond valence method for rapid screening of solid state ionic conductors, Canadian Chemistry Conference and Exhibition (CCCE 2022), June 2022.
• P. Sundarapura, M. Ihara, S. Manzhos. Density functional tight binding modeling of porous silicon for all-silicon tandem solar cells, Canadian Chemistry Conference and Exhibition (CCCE 2022), June 2022.
• S. Manzhos, M. Ihara. Insight with a black box method beyond automatic relevance determination with the help of high-dimensional model representation, International Symposium on Machine Learning in Quantum Chemistry (SMLQC), Nov. 2021.
• S. Manzhos, M. Ihara. Machine learning for orbital-free DFT, EU-Japan Workshop on HPC-based material sciences, Nov. 2021.

国内会議発表 (査読なし・不明)

• 前田 佳亮, Li Ruicheng, 亀田 恵佑, Manzhos Sergei, 伊原 学. 第一原理計算を用いた材料インフォマティクスにおける ペロブスカイト太陽電池の炭素系材料の検討, 第85回応用物理学会秋季学術講演会, Sept. 2024.
• 大歳 夏生, 葛西 祐也, 亀田 恵佑, Manzhos Sergei, 伊原 学. 日射成分とモジュール構造に基づく影計算による太陽電池リアルタイム発電量予測モデル, 化学工学会第55回秋季大会, Sept. 2024.
• 香川 達哉, 飯嶌 大樹, Lee Hyojae, Wang Shuai, 亀田 恵佑, Manzhos Sergei, 伊原 学. クラスタリングを利用したSHAP値の解析手法によるエネルギー消費行動の可視化, 化学工学会第55回秋季大会, Sept. 2024.
• Wang Shuai, 大屋 昌士, 大歳 夏生, 亀田 恵佑, 濱崎 博, (デロイトトーマツコンサルティング)大久保 辰哉, Manzhos Sergei, 伊原 学. 2050年のエネルギーシステム最適化に向けた全国建物壁面の太陽電池ポテンシャル算出, 化学工学会第55回秋季大会, Sept. 2024.
• 亀田 恵佑, 若宮 大志郎, 吉田 紗良, Chen Kexin, マンゾス セルゲイ, 伊原 学. 高効率と耐久性向上を両立させるカーボン空気二次電池システムのニッケルベース燃料極の開発, 化学工学会第55回秋季大会, Sept. 2024.
• 高木 伶海, 加藤 航太, 遠藤 明日香, Chen Kexin, 若宮 大志郎, 亀田 恵佑, Manzhos Sergei, 伊原 学. カーボン空気二次電池システムの充放電特性に対する温度依存性, 化学工学会第55回秋季大会, Sept. 2024.
• 亀田 恵佑, 中川 慶, 古賀 康友, Chen Kexin, 高木 伶海, 若宮 大志郎, 岡崎 成美, Lee Hyojae, 大歳 夏生, Manzhos Sergei, 伊原 学. 電極反応モデルに基づく水素発電におけるBaZr0.9Y0.1O3-δ添加Ni/YSZ燃料極の反応種被覆率の推定, 化学工学会 第89年会 (堺), Mar. 2024.
• Kexin Chen, Aulia Sukma Hutama, Keisuke Kameda, Sergei Manzhos, Manabu Ihara. Modulation of Molecular Adsorption Properties of Zirconia Nanoparticles: A Density Functional Tight Binding Theory Study, Mar. 2024.
• 岡本 卓磨, Anastassia Sorkin, 亀田 恵佑, Wang Hao, Sergei Manzhos, 伊原 学. Rutile型酸化チタンの自然発生的粒界形成の検討, 第71回応用物理学会春季学術講演会, Mar. 2024.
• Ruicheng Li, Keisuke Maeda, Man-Fai Ng, Keisuke Kameda, Sergei Manzhos, Manabu Ihara. Exploring Electronic Properties of Carbon Nanoflake-Based Materials for Charge Transport Layers in Perovskite Solar Cells: Insight from Solid-state Modelling, 第71回応用物理学会春季学術講演会, Mar. 2024.
• Chen Kexin, Kameda Keisuke, Koga Yasutomo, Manzhos Sergei, Ihara Manabu. Modelling of CO/CO2 Electrode Reactions in Carbon/Air Secondary Battery System, 化学工学会第89年会 (堺), Mar. 2024.
• 亀田 恵佑, 若宮 大志郎, Chen Kexin, Manzhos Sergei, 伊原 学. 炭素析出制御可能なカーボン空気二次電池システムの電極開発, 化学工学会 第89年会 (堺), Mar. 2024.
• 大歳 夏生, Lee Hyojae, 亀田 恵佑, Manzhos Sergei, 伊原 学. リアルタイム雲画像データに基づく日射成分を用いた影を含む太陽電池発電量予測モデル, 化学工学会 第89年会 (堺), Mar. 2024.
• 吉岡 大雄, 白倉 沙也加, 亀田 恵佑, Manzhos Sergei, 伊原 学. 熱・電力需要の変動が建物規模分散型水素蓄エネルギーシステムの経済性に与える影響, 化学工学会 第89年会 (堺), Mar. 2024.
• 岡崎 成美, 亀田 恵佑, Manzhos Sergei, 伊原 学. 変動型再エネの発電量変動に追従する水素製造方法としての水Pulse-jet固体酸化物電解セルの提案, 化学工学会第55回秋季大会, 2024.
• 若宮 大志郎, Chen Kexin, 亀田 恵佑, Manzhos Sergei, 伊原 学. カーボン空気二次電池システムにおける充放電特性のサーメット電極依存性, 化学工学会第55回秋季大会, 2024.
• Sergei Manzhos, Manabu Ihara. Machine learning beyond plain neural networks and kernel methods: from getting rid of non-linear optimization and overfitting to building many-body representations, Hierarchical Structure and Machine Learning (HISML) 2023, Oct. 2023.
• Lee Hyojae, 津田 舜作, 飯島 大樹, 亀田 恵佑, Manzhos Sergei, 伊原 学. .電力需要予測の高精度化に向けた高次元電力消費データのエンコーディング手法の提案, 化学工学会第54回秋季大会(福岡), Sept. 2023.
• Wang Shuai, 大屋 昌士, 亀田 恵佑, Manzhos Sergei, 伊原 学. 壁面設置による東京都の太陽光発電ポテンシャルの算出と日間電力変動抑制効果の検討, 化学工学会第54回秋季大会(福岡), Sept. 2023.
• Sergei Manzhos, Shunsaku Tsuda, Hyojae Lee, Manabu Ihara. Hybrid models combining neural networks (NN), Gaussian process regressions (GPR), and high-dimensional model representations (HDMR) for more powerful machine learning, The 37th Annual Conference of the Japanese Society for Artificial Intelligence (JSAI2023), June 2023.
• 大歳 夏生, Manzhos Sergei, 伊原 学. 影を含む太陽電池発電量のリアルタイム予測モデルと電力市場インバランスコストの試算, 化学工学会第88年会, Mar. 2023.
• Lee Hyojae, 津田 舜作, 飯嶌 大樹, Manzhos Sergei, 伊原 学. 建物内人間活動情報を含む高次元エネルギーデータを用いた電力需要予測モデルの提案, 化学工学会第88年会, Mar. 2023.
• 大屋 昌士, 汪帥, 濵﨑 博, Manzhos Sergei, 伊原 学. 配電領域を考慮した太陽光発電の導入ポテンシャルの精緻化による国内水素製造量の影響評価, 第42回水素エネルギー協会大会 (HESS大会), Nov. 2022.
• 飯嶌 大樹, Lee Hyojae, 津田 舜作, Manzhos Sergei, 伊原 学. 電力予測モデル構築に向けた高次元エネルギーデータのクラスタリング手法の検討と解析, 化学工学会第53回秋季大会, Sept. 2022.
• 津田 舜作, 大久保 辰哉, Lee Hyojae, 飯嶌 大樹, 大歳 夏生, Manzhos Sergei, 伊原 学. 汎用的な電力需要予測に向けた高次元エネルギーデータの分析と回帰モデルの検討, 化学工学会第53回秋季大会, Sept. 2022.
• 大久保 辰哉, (ENEOS) (法)原田 耕佑, (法)高見 洋史, Sergei Manzhos, 伊原 学. 卸電力市場からの電源調達を考慮した燃料電池を含む再エネ水素ステーションの最適設計, 化学工学会第53回秋季大会, Sept. 2022.
• 白倉 沙也加, 大久保 辰哉, (東芝) 松岡 敬・ (東芝エネルギーシステムズ) 松永 健太郎, (法)佐藤 純一, Manzhos Sergei, 伊原 学. 低炭素と経済性の両立に向けた建物規模分散型水素蓄エネルギーシステムの最適化設計, 化学工学会第53回秋季大会, Sept. 2022.
• 有賀 嵩晃, 亀田 恵佑, 伊藤 和真, Manzhos Sergei, 伊原 学. プロトン伝導体の原子価結合法計算に向けたニューラルネットワークモデルの適用, 化学工学会第53回秋季大会, Sept. 2022.
• Lee Hyojae, 津田 舜作, 飯嶌 大樹, Manzhos Sergei, 伊原 学. マイクログリッドにおける電力需要予測に向けた高次元データと時定数解析の検討, 化学工学会第53回秋季大会, Sept. 2022.
• 大屋 昌士, 濵﨑 博, Manzhos Sergei, 伊原 学. 配電領域における太陽光発電の導入ポテンシャルの精緻化及び経済最適化モデルを用いた導入影響評価, 化学工学会第53回秋季大会, Sept. 2022.
• Chen Kexin, Hasegawa Kei, Manzhos Sergei, Ihara Manabu, Waki Keiko. Study on Activity of Citric Acid-decorated Carbon Nanotubes for Oxygen Reduction Reaction, 化学工学会第87年会, Mar. 2022.
• 安元 慈瑛, 鈴木 一馬, Budiutama Gekko, 長谷川 馨, Manzhos Sergei, 伊原学. 熱分解を抑えた高速熱処理によるペロブスカイト活性層の大粒径化, 化学工学会第87年会, Mar. 2022.
• Chen Kexin, Hasegawa Kei, Manzhos Sergei, Ihara Manabu, Waki Keiko. Study on Activity of Citric Acid-decorated Carbon Nanotubes for Oxygen Reduction Reaction, 化学工学会第87年会, Mar. 2022.
• Chen Kexin, Hasegawa Kei, Manzhos Sergei, Ihara Manabu, Waki Keiko. Study on Activity of Citric Acid-decorated Carbon Nanotubes for Oxygen Reduction Reaction, 化学工学会第87年会, Mar. 2022.
• 有賀 嵩晃, 亀田 恵佑, 佐々木 瑛太, 長谷川馨, Manzhos Sergei, 伊原学. 原子価結合法への機械学習の適用によるペロブスカイト型プロトン伝導体探索の検討, 化学工学会第87年会, Mar. 2022.
• 大歳 夏生, 大久保 辰哉, 長谷川 馨, Manzhos Sergei, 伊原学. 影を含む太陽電池発電量予測における日射強度推定モデルと実測値による機械学習の検討, 化学工学会第87年会, Mar. 2022.
• Sundarapura Panus, Manzhos Sergei, Ihara Manabu. Modeling of the Effects of Porosity and Passivation on Porous Silicon, 化学工学会第87年会, Mar. 2022.
• Li Ruicheng, Hasegawa Kei, Manzhos Sergei, Ihara Manabu, Waki Keiko. Investigating the Structural Change of PbI2-rich Perovskite Solar Cells After Storage, 化学工学会第87年会, Mar. 2022.

その他の論文・著書など

• 亀田 恵佑, Sergei, 伊原 学. 炭素と二酸化炭素の酸化還元反応を利用した大容蓄電技術「カーボン空気二次電池システム」, 月刊 電設技術 -特集 二次電池の現状と用途およびライフサイクル- 4月号, Apr. 2024.
• 亀田恵佑, MANZHOS SERGEI, 伊原学. 固体酸化物燃料電池/電解セル材料としての高温プロトン伝導体の開発状況と計算化学の利用, 水素エネルギーシステム, 水素エネルギー協会（HESS), Vol. 48, No. 2, June 2023.

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